MIPDH: A Novel Computational Model for Predicting microRNA-mRNA Interactions by DeepWalk on a Heterogeneous Network

Abstract

Analysis of miRNA-target mRNA interaction (MTI) is of crucial significance in discovering new target candidates for miRNAs. However, the biological experiments for identifying MTIs have a high false positive rate and are high-priced, time-consuming, and arduous. It is an urgent task to develop effective computational approaches to enhance the investigation of miRNA-target mRNA relationships. In this study, a novel method called MIPDH is developed for miRNA-mRNA interaction prediction by using DeepWalk on a heterogeneous network. More specifically, MIPDH extracts two kinds of features, in which a biological behavior feature is learned using a network embedding algorithm on a constructed heterogeneous network derived from 17 kinds of associations among drug, disease, and 6 kinds of biomolecules, and the attribute feature is learned using the k-mer method on sequences of miRNAs and target mRNAs. Then, a random forest classifier is trained on the features combined with the biological behavior feature and attribute feature. When implementing a 5-fold cross-validation experiment, MIPDH achieved an average accuracy, sensitivity, specificity and AUC of 75.85, 74.37, 77.33%, and 0.8044, respectively. To further evaluate the performance of MIPDH, other classifiers and feature descriptors are conducted for comparisons. MIPDH can achieve a better performance. Additionally, case studies on hsa-miR-106b-5p, hsa-let-7d-5p, and hsa-let-7e-5p are also implemented. As a result, 14, 9, and 9 out of the top 15 targets that interacted with these miRNAs were verified using the experimental literature or other databases. All these prediction results indicate that MIPDH is an effective method for predicting miRNA-target mRNA interactions.

Figure 1

Quantity distribution of biological molecule associations.

Figure 2

Flowchart of the computational process of MIPDH based on the biological behavior and attribute.

Figure 3

ROC curves performed by the RF classifier based on integrated features of attribute and behavior.

Figure 4

ROC curves performed by the SVM classifier based on integrated features of attribute and behavior.

Figure 5

ROC curves performed by the LR classifier based on integrated features of attribute and behavior.

Figure 6

Performance comparison among RF, SVM, and LR models in terms of ROC curves and AUCs based on integrated features of attribute and behavior.

Figure 7

ROC curves performed by the RF classifier based on attribute features.

Figure 8

ROC curves performed by the RF classifier based on behavior features.

Figure 9

Performance comparison among behavior features, attribute features, and integrated features in terms of ROC curves and AUCs based on the RF classifier.